AI In Test Automation: Hype Meets Reality

Sam Syzov
Alina Ampilogova

The search for ways to facilitate and accelerate QA automation left many businesses enthusiastic about the growing use of artificial intelligence in test automation. Some decision-makers even consider AI-powered platforms the Holy Grail of QA automation, an ultimate solution for any challenges. 

While this assumption is good news for vendors providing AI tools on the market, can the same be said for the company’s productivity? Can artificial intelligence truly deliver everything stakeholders expect?

To outline the true capabilities of AI-powered tools for test automation, let’s explore their key features and limitations, and provide a realistic perspective on the advantages of AI testing.

Artificial intelligence in test automation

With artificial intelligence rapidly accelerating automation processes across various areas, it was only a matter of time before it reached test automation and introduced new levels of productivity and functionality.

Before the insertion of AI-powered technologies and machine learning, test automation was limited in its coverage of processes and tasks. It was mainly designed for repetitive tasks and could not process or update its knowledge base for handling issues encountered. 

Using artificial intelligence in automation testing allowed developers to create and distribute solutions that overcame the limitations of traditional automation tools.

  • New approach to data handling

While previous versions of test automation tools used to merely collate data, AI-powered tools understand and analyze the gathered data. It allows them to detect anomalies, learn patterns, and upgrade their repositories with new information to make decisions without human intervention. 

  • Unrestrained intelligence

AI testing tools don't follow preset rules—instead, they expand them based on the conclusions driven by data analysis. Such an approach grants them greater flexibility compared to their non-AI counterparts and enables them to benefit from performing repetitive tasks as their knowledge improves with each repetition. 

  • Reduced risk of failure

AI automation testing platforms make it possible to avoid downtime accumulation by providing fast responses to test case problems and fixing them without interrupting the process. Previous test automation tools didn't prevent QA from coming to a halt due to object identification issues. Those issues had to be fixed manually and the entire process had to be started anew. 

Such advantages made AI-powered automation testing tools highly popular in the solutions market, leading to many AI test automation platforms to choose from. 

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How can AI help with test automation challenges?

Manual testing and automated testing are two intertwined processes that are equally important to the end results. However, similar to many human-managed processes, manual testing has a number of points that, while not considered flaws, are still challenging to deal with: 

  • Nuanced team selection

Assembling and onboarding a team of experts with the right skills, experience, and understanding of the company’s business vision is a lengthy process. It requires communication and patience to select the professionals with the fitting mindset and ability to adapt. For that reason, business leaders often find themselves in need of a tool that can assist them while they work on expanding their team. 

  • Extensive QA process

To create a script for checking particular system behaviors, test automation experts needed to observe the interactions between the employees and the automated process and experiment with the right approach to creating a script. All those steps add extra hours to the QA phase.

  • Dependence on manual effort

Each test case has to be manually encoded as a script for automation testing. While it's already a procedure that takes a lot of cognitive resources, it also requires research of the company's processes for designing preconditions, test steps, input data, and output expectations. Even reusable components can’t be fully recycled for a similar project—the team has to manually write more code.

  • Regular script updates caused by UI changes

Whenever there are UI changes, businesses must support a smooth user experience by establishing synergy between visual components and functionality. However, such modifications can affect the test scripts and result in productivity coming to a halt. Therefore, the team needs to invest extra time in maintenance and monitoring. 

The emergence of AI-powered market solutions allowed companies to bypass those challenges.

benefits-of-ai in-software-testing

Facilitate QA with codeless testing

One of the core capabilities that allow AI testing tools to leverage test automation is codeless testing. AI-powered solutions have an interface that registers queries and business scenario descriptions, using them to create and execute test cases. 

The convenience of this feature lies in its improved accessibility: it is very simple to operate and can be used by stakeholders and product managers without deep technical knowledge. Within the framework of an AI testing tool, users only need basic HTML and CSS knowledge to run, edit, and maintain test cases.  

Due to this, using AI test automation tools enables companies to lower the learning curve and accelerate QA.

Additionally, unlike their previous iterations that enabled only a fraction of codeless features, modern AI-powered testing platforms provide the full spectrum of codeless testing options. 

Record and playback

Recording and rerunning activities users perform for imitating user activity in test cases.

Codeless test maintenance

The lack of code complexities makes tests maintenance possible even without complex programming language knowledge.

Built-in execution

Test cases are executed automatically, without manual intervention.


Reports on test results are generated by the platform in a comprehensible format, letting analysts instantly assess the progress.

Cure script fragility with self-healing

Previously, whenever a script broke due to an unidentified object property change, teams had to put new script development on hold and manually search for the object. 

Then they would have to update the script and start everything anew. Each of those steps took additional time to complete, further delaying the completion of automation testing. 

To prevent this, some automation scripts use built-in measures such as wildcards (special symbols for specifying variables, values, and dynamic page URLs) to handle property change. However, since wildcards were limited in their ability to account for all variations, it didn't completely eliminate the risk of script-breaking object changes. Faster and more accurate object identification required more intelligent and analytical technology—and AI testing platforms delivered exactly that with their self-healing feature.


Self-healing process doesn't rely on wildcards or presets. Instead, it launches a full-scale investigation. Whenever an object can't be identified due to a property name or value change, the AI testing tool extracts the object, similar objects, their values, and property names and puts them into temporary storage. 

There, it assigns a similarity score to each property based on their similarity with the missing objects' properties. Afterward, it calculates the total score and returns the object with the highest-scoring properties to the historical object repository file. Once the script identifies the new object, test execution proceeds while the repositories are updated with new information—enabling them to avoid the missing object issue in the future. The entire process takes seconds, and AI automation platforms can process up to 10 objects simultaneously. 

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Improve visual regression testing with artificial intelligence

Checking and monitoring the integrity of visual interfaces (website, app, and other digital dashboards) and their compliance with the designer's mockup (from components to colors) is yet another area that became positively influenced by AI. 

Manual visual regression testing involves layout comparison, i.e., scanning pages' structure or using a screenshot as a reference while going through each pixel and ensuring that potential inaccuracies are at most 2 pixels. While those methods can be applied during the early stages of development and allow a more scrupulous review of UI elements, they are still vulnerable to human error and prone to turning into tedious, time-consuming tasks. Not to mention, whenever there is a discrepancy, developers need to evaluate whether it's within acceptable limits or needs to be adjusted. 

For that reason, AI testing tools for visual regression testing become extremely helpful as they automatically make fast and accurate comparisons by running smart tests and generating reports on all the differences found. What makes automated visual regression testing particularly useful is that it doesn't consider acceptable differences and instantly marks them as acceptable, letting developers focus on discrepancies that affect the visual interface. Such swiftness and efficiency make AI-powered visual regression tools a good-to-have solution during visual interface design and launch, as well as visual interface upgrade and editing. 

Is there a limit to what AI-powered tools can do?

Given the advantages mentioned above, it may seem that the panacea for successful automation testing has been found, and human-managed testing is going to be a thing of the past. However, those conclusions are rather rushed. 

While AI-powered is much better at imitating and analyzing customer behavior and making decisions, "artificial intelligence" still refers to a smart tool rather than an independent expert mind. Thinking otherwise may lead to frustration and a drop in productivity as stakeholders try to introduce AI-powered automation tools to scenarios they aren't applicable to.

  • Low flexibility

While superior to non-AI test automation tools, AI-powered platforms still have limited functionality. They can scan through large datasets and identify new data, but dealing with new complicated tasks that introduce too many new variables is often beyond their algorithm capacity. In other words, AI-powered tools can only learn in patterns they were taught, but they can’t think outside the box. 

  • Reliance on human experience

Many users rely on AI to solve issues that human developers can’t. However, artificial intelligence isn't an artificial mind: it's a data-driven system based on approaches and problem-solving methods introduced by humans. Therefore, it only uses the data it was given to quickly evaluate, calculate, and provide solutions to a known issue. However, if an issue hasn't been solved through manual adjustment or testing before, AI won't be able to solve it either.  

  • No major issue coverage

Claims that AI-powered automation is the ultimate issue detector and problem-solver may create a dangerous mindset. Indeed, AI testing tools are very efficient at finding missing values, object identification, and pointing out not working buttons and broken links—yet those are minor issues. Issues like logic flaws or usability problems are not that easy to test—and therefore, they may slip by unnoticed, with product managers and other tool users being completely unaware. 

Given these limitations, it’s too early to laud AI automation testing tools as the full replacement of the QA team. At its current stage, artificial intelligence in test automation can be sufficient for certain tasks, while other cases would require human intervention. 

Ways to use AI in test automation

So, AI test automation is not the answer to all questions. For that reason, stakeholders need to be aware of when they can use AI-powered tools for closing their tasks and when they need to employ professional assistance from a qualified team. 

Given its advantages and limitation, artificial intelligence in automation performs at its best when it is used for:

1. Design testing

Due to their intelligent approach and fast response to issues and anomalies, AI testing tools work well for visual regression testing and considerably shorten the visual interface development time without sacrificing quality. Therefore, they are a good choice for website development and app development.

2. Testing non-complicated business scenarios

AI-powered tools can generate test use cases for simple business scenarios that won't be seeing any radical changes in the future. It will enable them to use the obtained data successfully and account for minor changes.

3. Simple UI testing

AI testing tools show great results in testing UI functionality as long as its structure isn't overcomplicated and doesn't have extra integrations. The more elements and complex tasks added, the harder it will be for the tool to stay flexible and adapt to the changes. 

4. Testing without an automation team at hand

Suppose a company needs minor testing done but doesn't have the experts to handle the task. In that case, AI test automation tools allow their PMs and non-technical experts to generate and execute tests and receive valuable data. Although they don't eliminate the necessity for hiring and onboarding a team of automation experts, AI testing tools still provide the coverage necessary for successful product performance.

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Artificial intelligence in test automation: does the hype live up to expectations?

There is no doubt that AI-powered platforms for automation testing will continue to change and evolve, overcoming their limitations and progressing toward better flexibility. However, this process will be slower than we expect, so the value of human intervention remains long-term and unchanged. Therefore, the promise of AI testing tools solving all problems is still a product of marketing bias rather than documented and proven facts. 

So, if there is a need to add changes to a mature and complicated UI and then test its performance, stakeholders still require something more than artificial intelligence. They need a flexible and experienced mind to evaluate their specifics, research their processes, and stay dedicated to test generation and maintenance. 

At that point, they need to switch from using market solutions to cooperating with test automation professionals capable of ensuring smooth change implementation and execution. 

If you need to improve your product’s quality and time-to-market with automated test generation and execution, let's chat! Get a detailed consultation with our test automation talents, who will provide you with the research, test case generation, and execution necessary for flawless product performance.


The role of artificial intelligence in test automation is not just to perform repetitive tasks but to learn and improve with each repetition. AI-powered tools identify minor issues, generate comprehensive reports, and optimize the time necessary to generate test cases.
Unlike other test automation platforms, AI-powered tools use machine learning that receives user commands and scenario descriptions that they then use to generate test cases. They also learn about sudden object properties change and store into repositories, which allows them to prevent similar issues from happening and ensure flawless test generation and execution. 
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